Anatomical primitive detection

ABSTRACT

A method of detecting an anatomical primitive in an image volume includes detecting a plurality of transformationally invariant points (TIPS) in the volume, aligning the volume using the TIPs, detecting a plurality landmark points in the aligned volume that are indicative of a given anatomical object, and fitting a target geometric primitive as the anatomical primitive based using the detected landmark points.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Application No.61/087,422, filed on Aug. 8, 2008 and U.S. Provisional Application No.61/101,794, filed on Oct. 1, 2008, wherein the disclosure of each areincorporated by reference herein.

BACKGROUND OF THE INVENTION

1. Technical Field

The present disclosure relates to anatomy detection and, morespecifically, to a system and method for transformation invariantlandmark detection for anatomical primitives.

2. Discussion of Related Art

Anatomical primitives such as points, planes, and regions of interestcan be an integral part of medical imaging analysis algorithms, such astracking, registration, segmentation, detection, and recognition. Inmany conventional detection approaches, these primitives are manuallydetected and labeled. However, these approaches are not generic, as theyfocus towards a particular plane and cannot adapt to handle abnormal,irregular and/or partial images. Further, these approaches may not workwell on real-world data. For example, the variability across patientscan be quite large, where many seemingly plausible heuristics wouldfail. Additionally, diseases or artifacts can alter/fade out aparticular anatomy. Furthermore, a partial field of view can lead topartial data problems.

Thus, there is a need for a detection system and method that is robustand generic to different variations, adaptable to differentapplications/problems and automatic to save time and improveconsistency/repeatability, for example, in follow-up studies of the samepatient or a cross-patient comparison.

SUMMARY OF THE INVENTION

According to an exemplary embodiment of the present invention, a methodof detecting an anatomical primitive in an image volume includesdetecting a plurality of transformationally invariant points (TIPs) inthe volume, aligning the volume using the TIPs, detecting a pluralitylandmark points in the aligned volume that are indicative of a givenanatomical object, and fitting a target geometric primitive as theanatomical primitive using the detected points.

The TIPs may be rotationally invariant points. The aligning may includealigning the volume based on the TIPs and annotated points (e.g., pointsof known location and identities) within a model patient volume. Priorto fitting of the geometric primitive, consensus voting may be performedon the detected landmark points to discard those some the previouslydetected ones deemed erroneous.

The fitting of the target geometric primitive may include, for apredetermined plurality of tries, randomly sampling points of thelandmark points (or those that remain if the consensus voting wasperformed) to fit a geometric primitive using a least squares method,refining the geometric primitive and generating a corresponding errorusing an iteratively re-weighted least squares (IRLS) method, andselecting one of the refined geometric primitives as the anatomicalprimitive based on the corresponding errors.

A set or subset of the steps before the fitting (e.g., detecting ofTIPs, aligning of the volume, detecting of landmark points, andperforming consensus voting) may be iteratively performed for apredetermined number of times. The random sampling may include samplinga minimum number of the remaining landmark points needed to fit thegeometric primitive. The selecting of the refined geometric primitivemay include selecting the refined primitive that has a lowest error ifits number of inlier points is greater than a predefined percentagetimes the number of remaining landmark points and the lowest error. Thepredefined percentage may indicate a probability that the remaininglandmark points are the inlier points. The number of inlier points maycorresponds to the positive weights generated by the IRLS methodcomputing weights of the geometric primitive using an M-estimatormethod. The IRLS method may include performing a re-weighted leastsquares method until the error increases.

The geometric primitive may be is one of a point, a curve (e.g., aline), a surface (e.g., a plane), or a three-dimensional region. Whenthe geometric primitive is a plane, the randomly sampling step mayinclude: selecting a reference point from the remaining landmark points,randomly sampling three non-neighbor landmark points relative to thereference point, and fitting a plane through the sampled points usingthe least squares method. Further, when the geometric primitive is aplane, the step of refining the geometric primitive may include:computing weights for the plane using an M-estimator method, refiningthe plane using the computed weights, and computing the error from therefined plane. The refining plane of the plane may include fitting theplane using a least squares method and the computed weights.

The discarding of the landmark points that were erroneously detectedusing consensus voting may include: dividing combinations of thelandmark points into voting groups, voting by each of the voting groupson landmark points of the other groups based on the degree to whichfeatures of the voting group match features of the landmark point votedupon, and discarding the landmark points that have a number of votesbelow a predetermined voting threshold value.

The model patient volume may include annotated landmark points of knownlocations and the aligning of the volume may then includes aligning theTIPs to the corresponding annotated landmark points. The volume may be acomputed tomography image or a magnetic resonance image.

According to an exemplary embodiment of the present invention, a methodof detecting an anatomical primitive in an image volume includes:detecting a plurality of transformationally invariant landmark points inthe image volume that are indicative of a given anatomical object andfitting a target geometric primitive as the anatomical primitive usingthe detected landmark points. When the geometric primitive is a plane,the fitting may include, for a predefined plurality of tries, randomlysampling three of the landmark points and generating a correspondingplane from the sampled points using a least squares method, using aniteratively re-weighted least squares (IRLS) method to refine the planeand generate a corresponding error, and selecting one of the refinedplanes as the detected anatomical primitive based on the errors.

The selecting of the refined plane may include selecting the one havinga lowest error if a number of its inlier points are greater than apredefined percentage times the number of landmark points. Using theIRLS method may include computing weights for the plane using anM-estimator method, generating the refined plane using the computedweights, and computing the error from the refined plane. The generatingof the refined plane may include fitting the plane using a least squaresmethod and the computed weights.

An exemplary embodiment of the present invention includes a computersystem comprising a processor and a program storage device readable bythe computer system, embodying a program of instructions executable bythe processor to perform method steps for detecting an anatomicalprimitive in an image volume. The method includes detecting a pluralityof transformationally invariant points (TIPs) in the volume, aligningthe volume using the TIPs, detecting a plurality landmark points in thealigned volume that are indicative of a given anatomical object, for apredefined plurality of tries, randomly sampling points of the remaininglandmark points to fit a geometric primitive using a least squaresmethod and using an iteratively re-weighted least squares (IRLS) methodto refine the geometric primitive and generating a corresponding error,and selecting one of the refined geometric primitives as the anatomicalprimitive based on the corresponding errors.

BRIEF DESCRIPTION OF THE DRAWINGS

Exemplary embodiments of the invention can be understood in more detailfrom the following descriptions taken in conjunction with theaccompanying drawings in which:

FIG. 1 illustrates a high-level flow chart of a method of detectinganatomical primitives, according to an exemplary embodiment of thepresent invention;

FIG. 2 illustrates a variant of the method of FIG. 1, according to anexemplary embodiment of the present invention;

FIG. 3 illustrates a high-level flow chart of a method for fitting atarget geometric primitive according to FIG. 1 and FIG. 2, according toan exemplary embodiment of the present invention;

FIG. 4 illustrates a high-level flow chart of a method for refining atarget geometric primitive and generating a corresponding erroraccording to FIG. 3, according to an exemplary embodiment of the presentinvention; and

FIG. 5 illustrates an example of a computer system capable ofimplementing the methods and systems according to embodiments of thepresent invention.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENTS

In describing exemplary embodiments of the present disclosureillustrated in the drawings, specific terminology is employed for sakeof clarity. However, the present disclosure is not limited to thespecific terminology selected, and it is to be understood that eachspecific element includes all technical equivalents which operate in asimilar manner. It is to be understood that the systems and methodsdescribed herein may be implemented in various forms of hardware,software, firmware, special purpose processors, or a combinationthereof.

In particular, at least a portion of the present invention may beimplemented as an application comprising program instructions that aretangibly embodied on one or more program storage devices (e.g., harddisk, magnetic floppy disk, RAM, ROM, CD ROM, etc.) and executable byany device or machine comprising suitable architecture, such as ageneral purpose digital computer having a processor, memory, andinput/output interfaces. It is to be further understood that, becausesome of the constituent system components and process steps depicted inthe accompanying Figures may be implemented in software, the connectionsbetween system modules (or the logic flow of method steps) may differdepending upon the manner in which the present invention is programmed.Given the teachings herein, one of ordinary skill in the related artwill be able to contemplate these and similar implementations of thepresent invention.

Exemplary embodiments of present invention seek to provide an approachfor automatically detecting anatomical primitives within a given imagevolume. The detected primitives include a number of landmark points,which can be used to determine a region of interest (ROI), such as apoint, a curve (e.g., a line), a surface (e.g., a plane), a 3D section,etc. For example, at least three landmark points are required to form aplane.

FIG. 1 illustrates a method of detecting an anatomical primitive in animage volume according to an exemplary embodiment of the presentinvention. Referring to FIG. 1, the method includes detecting aplurality of transformationally invariant points (TIPs) in a volume(S101), aligning the volume with using the TIPs (S102), detecting aplurality of landmark points in the aligned volume (S103), and fitting atarget geometric primitive as the anatomical primitive using thedetected landmark points (S104).

The method will be described in more detail below. The TIPs may berotationally invariant points (RIPs), which are points that are robustin the presence of rotation, e.g., the eyeball centers in MR (magneticresonance) head scout scans. The TIPs may instead be invariant withrespect to scale. A minimum number of TIPs that are needed to perform arigid alignment in the appropriate dimension are detected. For example,in two dimensions, generally at least two points are needed, while inthree dimensions, generally at least three points are needed. For a morerobust alignment, more TIP points are detected in addition to theminimum. However, due to occlusion or cropping, some of the detectedTIPs may be errors. The detected TIPs may be ordered in a list based ontheir quality, and a subset of the best quality TIPs may then be usedfor the alignment.

The alignment (block S102) may include aligning the current volume(e.g., a volume of the head) with a model patient (e.g., a model patientvolume), which includes pre-annotated landmarks. For example, thelocations and identities of the landmarks in the model patient volumemay have been pre-marked by a skilled health care profession and arethus known. While the model patient volume is typically not rotated, itmay contain a known rotation. The alignment may be performed by matchingup TIPs against corresponding known landmarks in the model patientvolume.

Detecting the landmark points (block S103) in the aligned image includesdetecting local feature candidates that are representative of potentialanatomical landmarks (e.g., tip of lungs). The points in the alignedvolume are considered Transformationally Aligned Points (TAPS). Thelocal feature candidates may be automatically detected from the alignedvolume by identifying regions in the volume that appear to be knownanatomical landmarks. The search may begin around an estimated positionfrom the model within a local patch, and then to a larger region. Theset of local feature candidates may include multiple local featurecandidates that appear to be the same anatomical landmark.

The detection of the TIPs and the landmark points may be performed usinga learning based algorithm. In an exemplary embodiment of the presentinvention, the points are detected in a coarse-to-fine manner. Forexample, in a course-to-fine detection, large blocks (e.g., those of acoarse level) of an image are first examined against a few pertinentfeatures to isolate a corresponding block of interest. Sub-blocks (e.g.,finer than the large blocks) of the isolated block of interest can thenbe examined against several more pertinent features. The process canthen be repeated on the sub-blocks until a desired level of quality isreached.

As shown by the optional dotted lines in FIG. 1, the methods of blocks101-103 may be iteratively performed for a predetermined number of timesto refine the detected landmark points. Alternately, the methods ofblocks 102-103 may be may be iteratively performed for a predeterminednumber of times to refine the detected landmark points (e.g., block 101of detecting the TIPs may be skipped in the iteration). In an alternateembodiment of the present invention, the methods of blocks 102 and 103are omitted and the fitting of block 104 is performed on the detectedplurality of TIPs. In another alternate embodiment of the presentinvention, the methods of blocks 101 and 102 are omitted, and block 103detects landmark points in the volume.

FIG. 2 illustrates a variant of the method FIG. 1, which performsconsensus voting on the detected landmark points to discard those itdeems to have been erroneously detected (S201). The method of block 201is performed after block 103 and before block 104. Similar to FIG. 1,steps before block 104 in FIG. 2 may be iteratively performed apredetermined number of times to refine the resulting landmark points.For example, the optional dotted lines in FIG. 2 show that blocksS101-S103, and S201 or blocks S101-S102 may be iteratively performed thepredetermined number of times.

The consensus voting is applied to the local feature candidates (block201) to remove local feature candidates that were erroneously detected.Each landmark is considered a candidate for the most reliable featureset. The quality of a candidate is voted upon by voting groups formed byother landmarks.

Each landmark may participate as an individual voter and may also formvoting groups with other landmarks. Ideally, if a candidate is good, itreceives a “YES” from good voting groups”, and a “NO” from bad voters.However, it is possible that a group of erroneous landmarks happens toform a legitimate constellation. In this example, if a landmark iserroneous, it receives a high vote from some bad voters in thelegitimate constellation.

A voting group may include only two other landmarks. Alternatively, eachvoting group may include a large number of landmarks. It is to beunderstood that the voting groups may be made up of any number of otherlandmarks, and it may also be possible to utilize voting groups ofdissimilar size.

In one voting strategy, erroneous detections are “peeled away” in asequential fashion. Each candidate receives a set of votes from othercandidates. The strategy then iteratively removes the worse candidate(e.g., the candidate whose maximum vote is a minimum across all theremaining candidates). This is repeated until the number of theremaining candidates reaches a pre-set value M as part of a min-maxremoval strategy. Assuming at least M good candidates, all the badcandidates can be removed. Exemplary pseudocode for implementing themin-max removal strategy is provided below in Table 1:

TABLE 1 for each candidate x_(i) do  Sort all the votes received bylandmark x_(i) end for repeat  {hacek over (x)} = arg max_(Xi) maxγ_(Xi)  Remove {hacek over (x)} and all votes involved with {hacek over(x)} Until Only M candidates are leftwhere the sorted array is defined by γ_(Xi).

While the above min-max strategy works well in many cases, differentstrategies may be employed for different behaviors, for example, whenmafia-like behavior is exhibited among erroneous detections. Accordingto a Mafia model, a collection of candidates/voters that are in truthbad, tend to give high votes to other members of the same collection. Inthis way, erroneous detections or bad voters may increase the likelihoodthat other bad voters are included. This may happen, for example, when aset of erroneous landmarks form a legitimate constellation.

Block 104 can use the resulting landmarks from block 103 or 201 togenerate an anatomic primitive. FIG. 3 and FIG. 4 illustrate anexemplary embodiment of block 104, which combines use of a randomsampling-based approach (RANSAC) and a robust regression approach (e.g.,M-estimation with iteratively re-weighted least squares (IRLS).

The following applies FIG. 3 and FIG. 4 to generate a plane n^(T)x=d asthe geometric primitive, where n is the normal to the plane, d is thedistance to the plane, and x is the point on the plane. Referring toFIG. 3, a random sampling of the minimum number of points needed to fita desired geometric primitive using least squares is performed (S301).For example, since a plane is used here, three (3) non-neighbor points(e.g., relative to a sampled region) are sampled from the landmarkpoints resulting from either the detection of block 103 or the consensusvoting of block 104. A plane n_(i),d_(i) is then fitted through thesampled points using the least squares method. The best fit in theleast-squares sense is that instance of the model for which the sum ofsquared residuals has its least value, a residual being the differencebetween an observed value and the value given by the model.

Once the geometric primitive (e.g., the plane) has been fitted throughthe sampled points, an iteratively re-weighted least squares (IRLS)method may be performed to refine the primitive (e.g., the plane) andgenerate a corresponding error (S302).

FIG. 4 illustrates an exemplary embodiment of the block 302, which maybe used to refine the primitives (e.g., planes) and generate thecorresponding errors. Referring to FIG. 4, weights of the fittedgeometric primitive (e.g., the plane) are computed based on anM-estimator (S401), a refined geometric primitive is generated usingweighted least squares (S402), and a new weighted error is generated forall inlier points of the refined primitive (e.g., the refined plane)(S403). The points of the refined plane with positive weights areconsidered the inliers. Blocks 401-403 may be performed iteratively forseveral tries MAX_ITER (e.g., 10) or until a predefined condition isreached. For example, in one embodiment of the present invention, theiteration terminates if the new weighted error increases.

Blocks 301-302 may be applied iteratively for several tries MAX_TRIES ondifferent sample points (e.g., three for a plane) to generate additionalrefined geometric primitives (e.g., planes) and corresponding errors oruntil a predetermined condition is reached. The parameter MAX_TRIES isan integer, which may be arbitrarily predefined to a set value (e.g.,10, 100, etc.) or determined empirically based on characteristics of theprimitive that is to be detected. One of the refined primitives (e.g.,planes) can then be selected based on the corresponding errors as thefinal anatomical primitive (S303). For example, the refined primitive(e.g., plane) with the lowest error may be selected. The parameterMAX_TRIES should be chosen to be sufficiently large to ensure a correctsolution.

Additionally, a pre-defined percentage P or likelihood that a given setof points N operated on by FIG. 3 and FIG. 4 may be used to filter outgeometric primitives (e.g., planes) incorrectly detected as havinginliers. For example, the generated refined geometric primitive (e.g.,plane) with the lowest error and having a number of liners greater thanP*N may be selected.

Exemplary pseudocode for implementing FIG. 3 and FIG. 4 for a plane isprovided below in Table 2:

TABLE 2 Given a set of points x_(i),i = (1,N), and percentage of inlierpoints P. for i from 1 to MAX_TRIES do  Randomly sample 3 non-neighborpoints and fit a plane ni,di through them using least squares for j from1 to MAX_ITER do  Compute weights based on the robust M-estimator.  Fitthe new plane ni,j di,j using the weighted least squares  Compute thenew weighted error for all the inlier points  Exit inner for loop if theerror increases end for  Pick the plane with the lowest error if numberof inliers is greater  than P * N end for

However, the methods of FIGS. 1-4 are not limited to being performed todetect anatomical planes, and can be adapted to detect geometricprimitives such as various surfaces, points, curves (e.g. lines), andregions of interest (e.g., cuboids). For example, each instance of planein the pseudocode of Table 2 can be replaced with the correspondingdesired primitive (e.g., surface, point, curve, line, cuboid, etc.) andthe number of points sampled, could be replaced with the minimum numberof points needed to fit the corresponding primitive (e.g., 2 for aline).

The resulting generated anatomical primitives may be used in medicalimaging analysis algorithms such as tracking, registration,segmentation, detection, recognition. For example, the methods may beused in the detection of the Mid-Sagittal (MSP), Optical Triangular (OT)planes of brain MR images, intervertebrae-plane detection for 3D spineMR application, detecting a meniscus plane in a knee MR image, etc.Further, the resulting primitives may be used in 3D medical imagingsystems (e.g., MR) to speed up imaging workflow. The methods and systemsherein disclosed can handle abnormal, irregular, and partial images.

FIG. 5 shows an example of a computer system, which may implement amethod and system of the present disclosure. The system and methods ofthe present disclosure, or part of the system and methods, may beimplemented in the form of a software application running on a computersystem, for example, a mainframe, personal computer (PC), handheldcomputer, server, etc. These software applications may be stored on acomputer readable media (such as hard disk drive memory 1008) locallyaccessible by the computer system and accessible via a hard wired orwireless connection to a network, for example, a local area network, orthe Internet.

The computer system referred to generally as system 1000 may include,for example, a central processing unit (CPU) 1001, a random accessmemory (RAM) 1004, a printer interface 1010, a display unit 1011, alocal area network (LAN) data transmission controller 1005, a LANinterface 1006, a network controller 1003, an internal bus 1002, and oneor more input devices 1009, for example, a keyboard, mouse etc. Asshown, the system 1000 may be connected to a data storage device, forexample, a hard disk, 1008 via a link 1007. CPU 1001 may be the computerprocessor that performs some or all of the steps of the methodsdescribed above (e.g., FIG. 1-4).

Embodiments of the present image are not limited to images of anyparticular format, size, or dimension. For example, the above methodsand system may be applied to images of various imaging formats such asmagnetic resonance image (MRI), computed tomography (CT), positronemission tomography (PET), etc. The images may be static images such assingle dimensional (1D), 2D, 3D, or moving images.

Although illustrative embodiments have been described herein withreference to the accompanying drawings, it is to be understood that thepresent invention is not limited to those precise embodiments, and thatvarious other changes and modifications may be affected therein by oneof ordinary skill in the related art without departing from the scope orspirit of the invention. All such changes and modifications are intendedto be included within the scope of the disclosure.

What is claimed is:
 1. A method of detecting an anatomical primitive inan image volume, the method comprising: detecting a plurality oftranformationally invariant points (TIPs) in the volume; aligning thevolume using the TIPs; detecting a plurality of landmark points in thealigned volume that are indicative of a given anatomical object; andfitting a target geometric primitive as the anatomical primitive usingthe detected landmark points, wherein the method is performed by aprocessor, wherein the fitting comprises: for a predetermined pluralityof tries, randomly sampling points of the landmark points to fit ageometric primitive using a least squares method; refining the geometricprimitive and generating a corresponding error using an iterativelyre-weighted least squares (IRLS) method; and selecting one of therefined geometric primitives as the anatomical primitive based on thecorresponding errors.
 2. The method of claim 1, wherein the TIPs arerotationally invariant points.
 3. The method of claim 1, wherein thealigning comprises aligning the volume based on the TIPs and annotatedpoints within a model patient volume.
 4. The method of claim 1, whereinprior to the fitting of the geometric primitive, performing consensusvoting on the detected landmark points to discard those it deemserroneous.
 5. The method of claim 1, wherein the random samplingcomprises sampling a minimum number of the landmark points needed to fitthe geometric primitive.
 6. The method of claim 4, wherein the consensusvoting comprises: dividing combinations of the landmark points intovoting groups; voting by each of the voting groups on landmark points ofthe other groups based on the degree to which features of the votinggroup match features of the landmark point voted upon; and discardingthe landmark points that have a number of votes below a predeterminedvoting threshold value.
 7. The method of claim 1, wherein the selectingof the refined geometric primitive comprises selecting the one that hasa lowest error if its number of inlier points is greater than apredefined percentage times the landmark points and the lowest error. 8.The method of claim 7, wherein the predefined percentage indicates aprobability that the landmark points are the inlier points.
 9. Themethod of claim 1, wherein the steps before the fitting are iterativelyperformed for a predetermined number of times.
 10. The method of claim7, wherein the number of inlier points corresponds to the positiveweights generated by the IRLS method computing weights of the geometricprimitive using an M-estimator method.
 11. The method of claim 1,wherein using the IRLS method comprises performing a re-weighted leastsquares method until the error increases.
 12. The method of claim 1,wherein the geometric primitive is one of a point, a curve, a surface,or a three-dimensional region.
 13. The method of claim 1, wherein therandomly sampling comprises: selecting a reference point from thelandmark points; randomly sampling three non-neighbor landmark pointsrelative to the reference point; and fitting a plane through the sampledpoints using the least squares method.
 14. The method of claim 13,wherein the refining the geometric primitive comprises: computingweights for the plane using an M-estimator method; refining the planeusing the computed weights; and computing the error from the refinedplane.
 15. The method of claim 14, wherein refining plane comprisesfitting the plane using a least squares method and the computed weights.16. The method of claim 1, realized as a computer readable mediumembodying instructions executed by a processor to perform the method fordetecting an anatomical primitive in an image volume.
 17. A method ofdetecting an anatomical primitive in an image volume, the methodcomprising: detecting a plurality of transformationally invariantlandmark points in the image volume that are indicative of a givenanatomical object; and fitting a target geometric primitive as theanatomical primitive using the detected landmark points, wherein themethod is performed by a processor, wherein when the target geometricprimitive is a plane, the fitting comprises: for a predefined pluralityof tries, randomly sampling three of the landmark points and generatinga corresponding plane from the sampled points using a least squaresmethod; using an iteratively re-weighted least squares (IRLS) method torefine the plane and generate a corresponding error; and selecting oneof the refined planes as the detected anatomical primitive based on theerrors.
 18. The method of claim 17, wherein using the IRLS methodcomprises performing a re-weighted least squares method until the errorincreases.
 19. The method of claim 18, wherein using the IRLS methodcomprises: computing weights for the plane using an M-estimator method,wherein the positive weights correspond to the inlier points; generatingthe refined plane using the computed weights; and computing the errorfrom the refined plane.
 20. The method of claim 19, wherein generatingthe refined plane comprises fitting the plane using a least squaresmethod and the computed weights.
 21. The method of claim 17, whereinselecting the refined plane comprises selecting the one having a lowesterror if a number of its inlier points are greater than a predefinedpercentage times the number of landmark points.
 22. A computer systemcomprising: a processor; and a program storage device readable by thecomputer system, embodying a program of instructions executable by theprocessor to perform method steps for detecting an anatomical primitivein an image volume, the method comprising: detecting a plurality oftransformationally invariant points (TIPs) in the volume; aligning thevolume using the TIPs; detecting a plurality landmark points in thealigned volume that are indicative of a given anatomical object; for apredefined plurality of tries, randomly sampling points of the remaininglandmark points to fit a geometric primitive using a least squaresmethod; and refining the geometric primitive and generating acorresponding error using an iteratively re-weighted least squares(IRLS) method; and selecting one of the refined geometric primitives asthe anatomical primitive based on the corresponding errors.